Analysis Of Biological Networks Research Articles

Inference of large modified Poisson-type graphical models: Application to RNA-seq data in childhood atopic asthma studies

Recent advances in next-generation sequencing technology have yielded huge amounts of transcriptomic data. The discreteness and the high dimensions of RNA-seq data have posed great challenges in biological network analysis. Although estimation theories for high-dimensional modified Poisson-type graphical models have been proposed for the network analysis of count-valued data, the statistical inference of these models is still largely unknown. We herein propose a two-step procedure in both edgewise and global statistical inference of these modified Poisson-type graphical models using a cutting-edge generalized low-dimensional projection approach for bias correction. Extensive simulations and a real example with ground truth illustrate asymptotic normality of edgewise inference and more accurate inferential results in multiple testing compared to the sole estimation and the inferential method under normal assumption. Furthermore, the application of our method to novel RNA-seq data of childhood atopic asthma in Puerto Ricans demonstrates more biologically meaningful results compared to the sole estimation and the inferential methods based on Gaussian and nonparanormal graphical models.

Visual exploration of large metabolic models.

Large metabolic models, including genome-scale metabolic models, are nowadays common in systems biology, biotechnology and pharmacology. They typically contain thousands of metabolites and reactions and therefore methods for their automatic visualization and interactive exploration can facilitate a better understanding of these models. We developed a novel method for the visual exploration of large metabolic models and implemented it in LMME (Large Metabolic Model Explorer), an add-on for the biological network analysis tool VANTED. The underlying idea of our method is to analyze a large model as follows. Starting from a decomposition into several subsystems, relationships between these subsystems are identified and an overview is computed and visualized. From this overview, detailed subviews may be constructed and visualized in order to explore subsystems and relationships in greater detail. Decompositions may either be predefined or computed, using built-in or self-implemented methods. Realized as add-on for VANTED, LMME is embedded in a domain-specific environment, allowing for further related analysis at any stage during the exploration. We describe the method, provide a use case and discuss the strengths and weaknesses of different decomposition methods. The methods and algorithms presented here are implemented in LMME, an open-source add-on for VANTED. LMME can be downloaded from www.cls.uni-konstanz.de/software/lmme and VANTED can be downloaded from www.vanted.org. The source code of LMME is available from GitHub, at https://github.com/LSI-UniKonstanz/lmme.

Effective Material Basis and Mechanism Analysis of Compound Banmao Capsule against Tumors Using Integrative Network Pharmacology and Molecular Docking.

Purpose Compound banmao capsule (CBC), a well-known traditional Chinese medical material, is known to inhibit various tumors. However, its material basis and pharmacological mechanisms remain to be elucidated. This study aimed to investigate the effective material basis and mechanisms of action of CBC against tumors. Methods Active compounds of CBC were identified using public database and reports to build a network. The corresponding targets of active compounds were retrieved from online databases, and the antitumor targets were identified by GeneCards database. The antitumor hub targets were generated via protein-protein interaction analysis using String, and key compounds and targets from the integrative network were detected by molecular docking and ADMET. Top targets in hepatocellular carcinoma were confirmed by quantitative real-time PCR (qPCR). Finally, the multivariate biological network was built to identify the integrating mechanisms of action of CBC against tumor cells. Results A total of 128 compounds and 436 targets of CBC were identified successfully. Based on the generated multivariate biological network analysis, 25 key compounds, nine hub targets, and two pathways were further explored. Effective material bases of cantharidin, baicalein, scutellarin, sesamin, and quercetin were verified by integrative network analysis. PTGS2, ESR1, and TP53 were identified as hub targets via multivariate biological network analysis and confirmed using qPCR. Furthermore, VEGF and estrogen signaling pathways seem to play a role in the antitumor activity of CBC. Thus, breast cancer may be a potential clinical indication of CBC. Conclusion This study successfully identified the material basis of CBC and its synergistic mechanisms of action against tumor cells.

Community Detection in Large-Scale Bipartite Biological Networks.

Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks—such as those that represent regulatory interactions, drug-gene, or gene-disease associations—are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a “hairball” of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.

Multi-omics approaches for revealing the complexity of cardiovascular disease.

The development and progression of cardiovascular disease (CVD) can mainly be attributed to the narrowing of blood vessels caused by atherosclerosis and thrombosis, which induces organ damage that will result in end-organ dysfunction characterized by events such as myocardial infarction or stroke. It is also essential to consider other contributory factors to CVD, including cardiac remodelling caused by cardiomyopathies and co-morbidities with other diseases such as chronic kidney disease. Besides, there is a growing amount of evidence linking the gut microbiota to CVD through several metabolic pathways. Hence, it is of utmost importance to decipher the underlying molecular mechanisms associated with these disease states to elucidate the development and progression of CVD. A wide array of systems biology approaches incorporating multi-omics data have emerged as an invaluable tool in establishing alterations in specific cell types and identifying modifications in signalling events that promote disease development. Here, we review recent studies that apply multi-omics approaches to further understand the underlying causes of CVD and provide possible treatment strategies by identifying novel drug targets and biomarkers. We also discuss very recent advances in gut microbiota research with an emphasis on how diet and microbial composition can impact the development of CVD. Finally, we present various biological network analyses and other independent studies that have been employed for providing mechanistic explanation and developing treatment strategies for end-stage CVD, namely myocardial infarction and stroke.

Transcriptome analysis of sinensetin-treated liver cancer cells guided by biological network analysis.

Hepatocellular carcinoma is recognized as one of the most frequently occurring malignant types of liver cancer globally, making the identification of biomarkers critically important. The aim of the present study was to identify the genes involved in the anticancer effects of flavonoid compounds so that they may be used as targets for cancer treatment. Sinensetin (SIN), an isolated polymethoxyflavone monomer compound, possesses broad antitumor activities in vitro. Therefore, the identification of a transcriptome profile on the condition of cells treated with SIN may aid to better understand the genes involved and its mechanism of action. Genomic profiling studies of cancer are increasing rapidly in order to provide gene expression data that can reveal prognostic biomarkers to combat liver cancer. In the present study, high-throughput RNA sequencing (RNA-seq) was performed to reveal differential gene expression patterns between SIN-treated and SIN-untreated human liver cancer HepG2 cells. A total of 43 genes were identified to be differentially expressed (39 downregulated and 4 upregulated in the SIN-treated group compared with the SIN-untreated group). An extensive network analysis for these 43 genes resulted in the identification of 10 upregulated highly interconnected hub genes that contributed to the progression of cancer. Functional enrichment analysis of these 10 hub genes revealed their involvement in the regulation of apoptotic processes, immune response and tumor necrosis factor production. Additionally, the mRNA expression levels of these 10 genes were evaluated using reverse transcription-quantitative PCR, and the results were consistent with the RNA-seq data. Overall, the results of the present study revealed differentially expressed genes involved in cancer after SIN treatment in HepG2 cells and may help to develop strategies targeting these genes for treating liver cancer.

Identifying Disease Related Genes by Network Representation and Convolutional Neural Network.

The identification of disease related genes plays essential roles in bioinformatics. To achieve this, many powerful machine learning methods have been proposed from various computational aspects, such as biological network analysis, classification, regression, deep learning, etc. Among them, deep learning based methods have gained big success in identifying disease related genes in terms of higher accuracy and efficiency. However, these methods rarely handle the following two issues very well, which are (1) the multifunctions of many genes; and (2) the scale-free property of biological networks. To overcome these, we propose a novel network representation method to transfer individual vertices together with their surrounding topological structures into image-like datasets. It takes each node-induced sub-network as a represented candidate, and adds its environmental characteristics to generate a low-dimensional space as its representation. This image-like datasets can be applied directly in a Convolutional Neural Network-based method for identifying cancer-related genes. The numerical experiments show that the proposed method can achieve the AUC value at 0.9256 in a single network and at 0.9452 in multiple networks, which outperforms many existing methods.

Integer programming formulations and efficient local search for relaxed correlation clustering

Relaxed correlation clustering (RCC) is a vertex partitioning problem that aims at minimizing the so-called relaxed imbalance in signed graphs. RCC is considered to be an NP-hard unsupervised learning problem with applications in biology, economy, image recognition and social network analysis. In order to solve it, we propose two linear integer programming formulations and a local search-based metaheuristic. The latter relies on auxiliary data structures to efficiently perform move evaluations during the search process. Extensive computational experiments on existing and newly proposed benchmark instances demonstrate the superior performance of the proposed approaches when compared to those available in the literature. While the exact approaches obtained optimal solutions for open problems, the proposed heuristic algorithm was capable of finding high quality solutions within a reasonable CPU time. In addition, we also report improving results for the symmetrical version of the problem. Moreover, we show the benefits of implementing the efficient move evaluation procedure that enables the proposed metaheuristic to be scalable, even for large-size instances.

Degree Adjusted Large-Scale Network Analysis Reveals Novel Putative Metabolic Disease Genes.

Simple SummaryTo explore some of the low-degree but topologically important nodes in the Metabolic disease (MD) network, we propose a background-corrected betweenness centrality (BC) and identify 16 novel candidates likely to play a role in MD. MD specific protein–protein interaction networks (PPINs) were constructed using two known databasesHuman Protein Reference Database (HPRD) and BioGRID. The identified candidates have been found to play a role in diverse conditions including co-morbidities of MD, neurological and immune system-related conditions.A large percentage of the global population is currently afflicted by metabolic diseases (MD), and the incidence is likely to double in the next decades. MD associated co-morbidities such as non-alcoholic fatty liver disease (NAFLD) and cardiomyopathy contribute significantly to impaired health. MD are complex, polygenic, with many genes involved in its aetiology. A popular approach to investigate genetic contributions to disease aetiology is biological network analysis. However, data dependence introduces a bias (noise, false positives, over-publication) in the outcome. While several approaches have been proposed to overcome these biases, many of them have constraints, including data integration issues, dependence on arbitrary parameters, database dependent outcomes, and computational complexity. Network topology is also a critical factor affecting the outcomes. Here, we propose a simple, parameter-free method, that takes into account database dependence and network topology, to identify central genes in the MD network. Among them, we infer novel candidates that have not yet been annotated as MD genes and show their relevance by highlighting their differential expression in public datasets and carefully examining the literature. The method contributes to uncovering connections in the MD mechanisms and highlights several candidates for in-depth study of their contribution to MD and its co-morbidities.

Connectedness matters: construction and exact random sampling of connected networks

We describe a new method for the random sampling of connected networks with a specified degree sequence. We consider both the case of simple graphs and that of loopless multigraphs. The constraints of fixed degrees and of connectedness are two of the most commonly needed ones when constructing null models for the practical analysis of physical or biological networks. Yet handling these constraints, let alone combining them, is non-trivial. Our method builds on a recently introduced novel sampling approach that constructs graphs with given degrees independently (unlike edge-switching Markov chain Monte Carlo methods) and efficiently (unlike the configuration model), and extends it to incorporate the constraint of connectedness. Additionally, we present a simple and elegant algorithm for directly constructing a single connected realization of a degree sequence, either as a simple graph or a multigraph. Finally, we demonstrate our sampling method on a realistic scale-free example, as well as on degree sequences of connected real-world networks, and show that enforcing connectedness can significantly alter the properties of sampled networks.

Epihet for intra-tumoral epigenetic heterogeneity analysis and visualization

Intra-tumoral epigenetic heterogeneity is an indicator of tumor population fitness and is linked to the deregulation of transcription. However, there is no published computational tool to automate the measurement of intra-tumoral epigenetic allelic heterogeneity. We developed an R/Bioconductor package, epihet, to calculate the intra-tumoral epigenetic heterogeneity and to perform differential epigenetic heterogeneity analysis. Furthermore, epihet can implement a biological network analysis workflow for transforming cancer-specific differential epigenetic heterogeneity loci into cancer-related biological function and clinical biomarkers. Finally, we demonstrated epihet utility on acute myeloid leukemia. We found statistically significant differential epigenetic heterogeneity (DEH) loci compared to normal controls and constructed co-epigenetic heterogeneity network and modules. epihet is available at https://bioconductor.org/packages/release/bioc/html/epihet.html.

Network propagation for biological network analysis

Network propagation for biological network analysis

Biological Regulatory Network (BRN) Analysis and Molecular Docking Simulations to Probe the Modulation of IP3R Mediated Ca2+ Signaling in Cancer.

Inositol trisphosphate receptor (IP3R) mediated Ca+2 signaling is essential in determining the cell fate by regulating numerous cellular processes, including cell division and cell death. Despite extensive studies about the characterization of IP3R in cancer, the underlying molecular mechanism initiating the cell proliferation and apoptosis remained enigmatic. Moreover, in cancer, the modulation of IP3R in downstream signaling pathways, which control oncogenesis and cancer progression, is not well characterized. Here, we constructed a biological regulatory network (BRN), and describe the remodeling of IP3R mediated Ca2+ signaling as a central key that controls the cellular processes in cancer. Moreover, we summarize how the inhibition of IP3R affects the deregulated cell proliferation and cell death in cancer cells and results in the initiation of pro-survival responses in resistance of cell death in normal cells. Further, we also investigated the role of stereo-specificity of IP3 molecule and its analogs in binding with the IP3 receptor. Molecular docking simulations showed that the hydroxyl group at R6 position along with the phosphate group at R5 position in ‘R’ conformation is more favorable for IP3 interactions. Additionally, Arg-266 and Arg-510 showed π–π and hydrogen bond interactions and Ser-278 forms hydrogen bond interactions with the IP3 binding site. Thus, they are identified as crucial for the binding of antagonists.

Network Biological Modeling: A Novel Approach to Interpret the Traditional Chinese Medicine Theory of Exterior-Interior Correlation Between the Lung and Large Intestine

ObjectiveTo study the common pathogenesis of pneumonia and colitis using modern biological network analysis tools, and to explore the theory that the lung and large intestine are exteriorly and interiorly related. MethodsThe relevant target genes (hereinafter, “targets”) of pneumonia and colitis were separately queried on the GeneCards database. The main targets of the two diseases were then screened out according to their correlation scores and intersected to obtain those common to the two diseases. Metascape was used to analyze the main and common targets identified, and the Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to enrich and analyze the common targets. Cytoscape 3.7.2 software was used to build the network diagram. ResultsIn total, 54 targets, such as TNF, IL-10, IL-6, IL-2, IL-4, TLR4, TLR2, CXCL8, IL-17A and IFNG, etc., are common to pneumonia and colitis, which are mainly enriched in these processes such as cytokine–cytokine receptor interaction, the T-cell receptor signaling pathway, the Toll-like receptor signaling pathway and the Jak-STAT signaling pathway. The Metascape modular analysis identified 11 modules for pneumonia, six modules for colitis, and two modules for the common targets. ConclusionsPneumonia and colitis have the same pathogenic targets and mechanisms of action and finally interact with each other through inflammatory reactions and immune responses. This provides a probable molecular mechanism that explains the theory that the lung and large intestine are exteriorly and interiorly related.

Biological network analysis with deep learning.

Recent advancements in experimental high-throughput technologies have expanded the availability and quantity of molecular data in biology. Given the importance of interactions in biological processes, such as the interactions between proteins or the bonds within a chemical compound, this data is often represented in the form of a biological network. The rise of this data has created a need for new computational tools to analyze networks. One major trend in the field is to use deep learning for this goal and, more specifically, to use methods that work with networks, the so-called graph neural networks (GNNs). In this article, we describe biological networks and review the principles and underlying algorithms of GNNs. We then discuss domains in bioinformatics in which graph neural networks are frequently being applied at the moment, such as protein function prediction, protein–protein interaction prediction and in silico drug discovery and development. Finally, we highlight application areas such as gene regulatory networks and disease diagnosis where deep learning is emerging as a new tool to answer classic questions like gene interaction prediction and automatic disease prediction from data.

Phytochemicals induce apoptosis by modulation of nitric oxide signaling pathway in cervical cancer cells.

Nitric Oxide (NO) is produced by NO synthases (NOS) and is a key signaling molecule that regulates tumorigenesis, both aiding and alleviating it. Elevated NO levels are cytotoxic to cancer cells, making NOS an important target for cancer treatment. In the present study, the modulatory effects of the phytochemicals, quercetin, sulforaphane, genistein, and epigallocatechin-3-gallate on NO pathway and apoptosis were shown in HeLa cervical cancer cells. Fluorescent microscopy and flow cytometry were used to assess apoptosis. A Griess assay was used to quantitatively measure NO, quantitative PCR array was used to assess the expression levels of genes involved in the NO signaling pathway, and immunocytochemistry was used to determine NOS protein expression. The functional association among the modulated genes was evaluated using network biology analysis, gene set enrichment, and KEGG pathway analysis. Treatment with the phytochemicals elevated NO levels in HeLa cells and modulated various genes involved in nitric oxide biosynthesis, superoxide metabolism, and oxidative stress, including NOS1, NOS2, NOS3, ALOX12, and SOD2, with a concomitant increase in NOS2 and NOS3 protein expression levels; also, the phytochemicals were found to induce apoptosis. These results suggest that the phytochemical-induced cell death is partially attributed to the activation of the NO pathway and upregulation of pro-oxidant ROS generators. Further experimental studies are required to explore this mechanistic association of NO signaling pathway activation and induction of apoptosis in other types of cancer.

Multiscale community detection in Cytoscape.

Detection of community structure has become a fundamental step in the analysis of biological networks with application to protein function annotation, disease gene prediction, and drug discovery. This recent impact creates a need to make these techniques and their accompanying visualization schemes available to a broad range of biologists. Here we present a service-oriented, end-to-end software framework, CDAPS (Community Detection APplication and Service), that integrates the identification, annotation, visualization, and interrogation of multiscale network communities, accessible within the popular Cytoscape network analysis platform. With novel design principles, CDAPS addresses unmet new challenges, such as identifying hierarchical community structures, comparison of outputs generated from diverse network resources, and easy deployment of new algorithms, to facilitate community-sourced science. We demonstrate that the CDAPS framework can be applied to high-throughput protein-protein interaction networks to gain novel insights, such as the identification of putative new members of known protein complexes.

Pyntacle: a parallel computing-enabled framework for large-scale network biology analysis.

BackgroundSome natural systems are big in size, complex, and often characterized by convoluted mechanisms of interaction, such as epistasis, pleiotropy, and trophism, which cannot be immediately ascribed to individual natural events or biological entities but that are often derived from group effects. However, the determination of important groups of entities, such as genes or proteins, in complex systems is considered a computationally hard task.ResultsWe present Pyntacle, a high-performance framework designed to exploit parallel computing and graph theory to efficiently identify critical groups in big networks and in scenarios that cannot be tackled with traditional network analysis approaches.ConclusionsWe showcase potential applications of Pyntacle with transcriptomics and structural biology data, thereby highlighting the outstanding improvement in terms of computational resources over existing tools.

Effect of Asthma on Erectile Dysfunction in Rats as Determined by Biological Network Analysis.

BackgroundWe explored the effect of asthma on erectile dysfunction (ED) and the effects of the expression of related proteins.Material/MethodsWe used a bioinformatics database to predict the targets and pathways associated with asthma and ED. The rat model of asthma was caused by an ovalbumin solution. The number of erections in 30 min was observed by injecting apomorphine into the neck at a dose of 100 μg/kg. Rats with no erection were regarded as the model group (group B), and the previous random 6 normal rats were regarded as the control group (group A). We used hematoxylin and eosin (HE) to compare the tissue structure of the cavernous body of the penis. Real-time quantitative polymerase chain reaction (RT-qPCR) and western blotting were used to determine the expression levels of insulin (INS), interleukin 6 (IL6), albumin (ALB), tumor necrosis factor (TNF), and vascular endothelial growth factor A (VEGFA) at both the protein and messenger ribonucleic acid (mRNA) levels.ResultsHE staining results show that compared with group A, the blood sinus distribution of the cavernous body in group B was disordered, and the density of endothelial cells and smooth muscle cells decreased significantly. Western blotting and RT-qPCR showed that the levels of IL6, TNF, and VEGFA protein and mRNA in group B were significantly higher (P<0.05) than those in group A. The levels of INS and ALB were not significantly different between the 2 groups.ConclusionsOn the basis of the results, we found that asthma caused pathological changes in the penises of rats and led to reduced erectile function via changes in the expression of IL6, TNF, and VEGFA proteins.

PMINR: Pointwise Mutual Information-Based Network Regression - With Application to Studies of Lung Cancer and Alzheimer's Disease.

Complex diseases are believed to be the consequence of intracellular network(s) involving a range of factors. An improved understanding of a disease-predisposing biological network could lead to better identification of genes and pathways that confer disease risk and therefore inform drug development. The group difference in biological networks, as is often characterized by graphs of nodes and edges, is attributable to effects of these nodes and edges. Here we introduced pointwise mutual information (PMI) as a measure of the connection between a pair of nodes with either a linear relationship or nonlinear dependence. We then proposed a PMI-based network regression (PMINR) model to differentiate patterns of network changes (in node or edge) linking a disease outcome. Through simulation studies with various sample sizes and inter-node correlation structures, we showed that PMINR can accurately identify these changes with higher power than current methods and be robust to the network topology. Finally, we illustrated, with publicly available data on lung cancer and gene methylation data on aging and Alzheimer’s disease, an evaluation of the practical performance of PMINR. We concluded that PMI is able to capture the generic inter-node correlation pattern in biological networks, and PMINR is a powerful and efficient approach for biological network analysis.